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reading-plus-ai

mcp-server-data-exploration

by reading-plus-ai
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Python
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MCP Server for Data Exploration

MCP Server is a versatile tool designed for interactive data exploration.

Your personal Data Scientist assistant, turning complex datasets into clear, actionable insights.

🚀 Try it Out

  1. Download Claude Desktop

  2. Install and Set Up

    • On macOS, run the following command in your terminal:

    python setup.py

  3. Load Templates and Tools

    • Once the server is running, wait for the prompt template and tools to load in Claude Desktop.

  4. Start Exploring

    • Select the explore-data prompt template from MCP

    • Begin your conversation by providing the required inputs:

      • csv_path: Local path to the CSV file

      • topic: The topic of exploration (e.g., "Weather patterns in New York" or "Housing prices in California")

Related MCP server: MCP Tabular Data Analysis Server

Examples

These are examples of how you can use MCP Server to explore data without any human intervention.

Case 1: California Real Estate Listing Prices

Watch the video

Case 2: Weather in London

📦 Components

Prompts

  • explore-data: Tailored for data exploration tasks

Tools

  1. load-csv

    • Function: Loads a CSV file into a DataFrame

    • Arguments:

      • csv_path (string, required): Path to the CSV file

      • df_name (string, optional): Name for the DataFrame. Defaults to df_1, df_2, etc., if not provided

  2. run-script

    • Function: Executes a Python script

    • Arguments:

      • script (string, required): The script to execute

⚙️ Modifying the Server

Claude Desktop Configurations

  • macOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%/Claude/claude_desktop_config.json

Development (Unpublished Servers)

"mcpServers": {
  "mcp-server-ds": {
    "command": "uv",
    "args": [
      "--directory",
      "/Users/username/src/mcp-server-ds",
      "run",
      "mcp-server-ds"
    ]
  }
}

Published Servers

"mcpServers": {
  "mcp-server-ds": {
    "command": "uvx",
    "args": [
      "mcp-server-ds"
    ]
  }
}

🛠️ Development

Building and Publishing

  1. Sync Dependencies

    uv sync

  2. Build Distributions

    uv build

    Generates source and wheel distributions in the dist/ directory.

  3. Publish to PyPI

    uv publish

🤝 Contributing

Contributions are welcome! Whether you're fixing bugs, adding features, or improving documentation, your help makes this project better.

Reporting Issues

If you encounter bugs or have suggestions, open an issue in the issues section. Include:

  • Steps to reproduce (if applicable)

  • Expected vs. actual behavior

  • Screenshots or error logs (if relevant)

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

💬 Get in Touch

Questions? Feedback? Open an issue or reach out to the maintainers. Let's make this project awesome together!

About

This is an open source project run by ReadingPlus.AI LLC. and open to contributions from the entire community.

Available Tools

2 tools
load_csvB

Load CSV File Tool

Purpose: Load a local CSV file into a DataFrame.

Usage Notes: • If a df_name is not provided, the tool will automatically assign names sequentially as df_1, df_2, and so on.

ParametersJSON Schema
NameRequiredDescriptionDefault
csv_pathYes
df_nameNo

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the automatic naming behavior when df_name is omitted, which is helpful. However, it doesn't address critical behavioral aspects like error handling, file format requirements, memory implications, or what happens if the CSV path is invalid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Purpose, Usage Notes) and uses bullet points efficiently. Both sentences earn their place by providing essential information without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a data loading tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It doesn't explain what a DataFrame is in this context, what the tool returns, error conditions, or file format requirements. The description should provide more complete operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the schema provides no parameter documentation. The description partially compensates by explaining the df_name parameter's behavior when omitted, but doesn't clarify csv_path requirements or format. It adds some value but doesn't fully compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Load a local CSV file into a DataFrame' with specific verb ('Load') and resource ('CSV file'), making it immediately understandable. However, it doesn't differentiate from the sibling tool 'run_script', which appears unrelated but could potentially handle similar data operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage guidance through the 'Usage Notes' section about automatic naming when df_name isn't provided. However, it lacks explicit guidance on when to use this tool versus alternatives or any prerequisites for successful operation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

run_scriptB

Python Script Execution Tool

Purpose: Execute Python scripts for specific data analytics tasks.

Allowed Actions 1. Print Results: Output will be displayed as the script’s stdout. 2. [Optional] Save DataFrames: Store DataFrames in memory for future use by specifying a save_to_memory name.

Prohibited Actions 1. Overwriting Original DataFrames: Do not modify existing DataFrames to preserve their integrity for future tasks. 2. Creating Charts: Chart generation is not permitted.

ParametersJSON Schema
NameRequiredDescriptionDefault
scriptYes
save_to_memoryNo

TDQS

B3.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: output is displayed as stdout, optional saving of DataFrames in memory, and prohibitions on overwriting original DataFrames and creating charts. This covers execution behavior, memory handling, and constraints, though it doesn't address error handling, performance limits, or security aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections ('Purpose', 'Allowed Actions', 'Prohibited Actions'), making it easy to scan. It's appropriately sized without unnecessary fluff, though the 'Purpose' section could be more concise. Every sentence adds value, such as clarifying output behavior and constraints.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a script execution tool with no annotations and no output schema, the description is moderately complete. It covers execution purpose, allowed/prohibited actions, and some parameter context, but lacks details on error handling, return values, or integration with the sibling tool. For a tool with 2 parameters and significant behavioral implications, more completeness is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'save_to_memory' in the 'Allowed Actions' section, adding some meaning beyond the schema. However, it doesn't explain the 'script' parameter's content or format, leaving a key parameter undocumented. With 2 parameters and low coverage, the description only partially compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Execute Python scripts for specific data analytics tasks,' providing a specific verb ('Execute') and resource ('Python scripts'). It distinguishes from the sibling tool 'load_csv' by focusing on script execution rather than data loading. However, it doesn't specify what 'specific data analytics tasks' entail, keeping it slightly vague.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage guidance through 'Allowed Actions' and 'Prohibited Actions' sections, suggesting when to use certain features like saving DataFrames and when to avoid actions like chart generation. However, it lacks explicit guidance on when to use this tool versus the sibling 'load_csv' or other alternatives, and doesn't mention prerequisites or specific contexts for use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: load_csv is for loading CSV files into DataFrames, while run_script is for executing Python scripts for data analytics tasks. There is no overlap in functionality, and an agent would easily distinguish between them.

Naming Consistency4/5

Both tools use snake_case naming, which is consistent, but they follow different patterns: load_csv uses a verb_noun format, while run_script uses verb_noun as well but with a more generic noun. This minor deviation keeps it mostly consistent but not perfectly aligned.

Tool Count2/5

With only two tools, the server feels severely under-scoped for data exploration. Key operations like data transformation, filtering, aggregation, or visualization are missing, making it incomplete for typical data analysis workflows.

Completeness2/5

The tool set is highly incomplete for data exploration. It covers only loading data and running scripts, with no tools for common tasks like data cleaning, analysis, or exporting results. This will likely cause agent failures when trying to perform comprehensive data exploration.

Maintenance

ActivityInactive
ResponsivenessSyncing

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